Biblio

Research and Development, and information management professionals routinely employ simple keyword searches or more complex Boolean queries when using databases such as PubMed and Ovid and search engines like Google to find the information they need. While satisfying the basic needs of the researcher, basic search is limited which can adversely affect both precision and recall, decreasing productivity and damaging the researchers' ability to discover new insights. The cloud service providers who store user's data may access sensitive information without any proper authority. A basic approach to save the data confidentiality is to encrypt the data. Data encryption also demands the protection of keyword privacy since those usually contain very vital information related to the files. Encryption of keywords protects keyword safety. Fuzzy keyword search enhances system usability by matching the files perfectly or to the nearest possible files against the keywords entered by the user based on similar semantics. Encrypted keyword search in cloud using this logic provides the user, on entering keywords, to receive best possible files in a more secured manner, by protecting the user's documents.

Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.

Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.